Present Technique Research Articles

Marginalisasi Perempuan Dalam Novel Nyutrayu Karya Joko Gesang Santoso

The purpose of this research is to describe the marginalization of women in the novel "Nyutrayu" by Joko Gesang Santoso. The research method used is qualitative descriptive method. The presentation technique of the research results uses the note-taking technique. The results show various forms of women's marginalization such as women becoming victims of patriarchal power where men hold more authority, the belief that women should not surpass men, being ostracized due to physical differences such as having blue eyes which creates the perception that blue-eyed individuals are foreigners and not indigenous, the attitude of demeaning women leading to their social exclusion, and the impoverishment causing women to lose their belongings. Female characters experiencing marginalization in the novel "Nyutrayu" include Aran, Sumi, and Lara, which can be seen through their behavior, dialogues, portrayal through other characters, thoughts, and dialogues among characters. These characters experience marginalization, leading them to choose a life of silence, gouging out their own eyes to avoid seeing others' past and future, resigning themselves to being objectified by men to satisfy male sexuality, and allowing themselves to be marginalized by living on the outskirts of a village far from the community.

‘It looks better than a bowl of mush’: Views on the use of food design strategies, including 3D food printing, to improve meals for people with dysphagia

To improve meals for people with dysphagia, we explored the views of people with dysphagia, their supporters and allied health professionals on a range of food design strategies (e.g. food shaping and food presentation techniques), including 3D food printing. From November 2021 to February 2022, an online survey of (1) adults with dysphagia (n = 30) and (2) supporters of people with dysphagia and allied health professionals (n = 22) was conducted. The survey included multiple choice, Likert scale and open-ended questions. Data was analysed descriptively. Most participants across the two groups had used at least one food design strategy for texture-modified foods and none had used 3D food printing. People with dysphagia were less likely to use food shaping techniques in preparing their texture-modified meals than other respondents. Supporters of people with dysphagia and allied health professionals were more likely than people with dysphagia to use food shaping techniques and to consider that 3D food printing could improve the visual appeal and enjoyment of texture-modified foods. A range of issues impacting the feasibility of 3D food printing were identified. The use of food design strategies for texture-modified foods may increase the food choices and mealtime enjoyment of people with dysphagia. Further research exploring how people with dysphagia and their supporters engage with 3D food printing could identify further influences on their future use of these technologies.

Controllable Double‐Emulsion Droplet Manipulation Actuated by a Triboelectric Nanogenerator

AbstractElectrically controlled droplet manipulation is attractive for lots of applications ranging from material synthesis and drug delivery. However, the present techniques using bulky and expensive power supplies to generate high‐voltage electrical signals are easy to cause electrode electrolysis, bubble formation in buffer solution, and are incapable for point‐of‐care testing. Herein, a novel and portable electrical method for self‐powered emulsion droplet core coalescence and release based on triboelectric nanogenerator (TENG) is presented. The electrical signal with a 2.25 kV open‐voltage and a 35 µA short‐circuit current can be conveniently generated via frictional electrification effect, and the harmful electrochemical reaction in solutions can be avoided. The high transient pulse voltage and stable sinusoidal signal between two electrodes in microfluidic device can generate a strong electric field, which causes apparent Maxwell electrical stresses at droplet interfaces and subsequently promotes the core coalescence and release of double‐emulsions. By adjusting the voltage magnitude, the core coalescence and release behaviors of double‐emulsion droplets can be controllably achieved, followed by the synthesis copper‐based metal–organic framework (Cu‐MOF) nanoparticles. Thus, this portable, effective and self‐powered droplet manipulation technique can be attractive for those droplet‐based applications.

The Influence Of Social Media Marketing And Brand Engagement On Brand Awareness And Brand Image (Case Study On Brownis Ingsun Products On The Account @sorgumnesia.official)

Social Media Marketing and Brand Engagement have an important role for business which has an impact on Brand Awareness and Brand Image. This research aims to identify the influence of social media marketing and brand engagement on brand awareness and brand image (case study of the brownis ingsun product on the @sorgumnesia.official account). The population in this research are Instagram followers @sorgumnesia.officia and consumers who have purchased products from Sorgumnesia, namely "Brownis Ingsun". This research sample consisted of 120 respondents selected using purposive sampling technique. The data presentation technique used is quantitative. The data analysis technique uses multiple linear regression analysis. The results of this research show that Social Media Marketing partially has a significant effect on Brand Awareness, Brand Engagement partially has a significant effect on Brand Awareness, Social Media Marketing partially has no significant effect on Brand Image, Brand Engagement partially has a significant effect on Brand Image, and Brand Awareness partially has a significant effect on Brand Image. Then, Social Media Marketing and Brand Engagement simultaneously have a significant effect on Brand Awareness and Social Media Marketing and Brand Engagement simultaneously have a significant effect on the Brand Image of Brownis Ingsun Products on the @sorgumnesia.official account.

The Influence Of Advertisement Appeal, Product Quality, And Brand Equity On Head & Shoulders Shampoo Purchase Decision In Surabaya

Purchasing decisions play an important role for companies as they impact profitability and business sustainability. This research aims to identify the influence of advertising appeal, product quality, and brand equity on the purchase decisions of Head & Shoulders shampoo products in Surabaya. The population of this study consists of Head & Shoulders shampoo users who have also watched the "Joe Taslim x Fadil Jaidi - Who Will Be the Next BA Head & Shoulders?" advertisement and are aged between 15 and 64 in Surabaya. The research sample consists of 155 respondents selected using purposive sampling technique. Quantitative data presentation technique was employed. The research analysis uses multiple regression analysis. The research results show that advertising appeal, product quality, and brand equity have a significant effect on purchasing decisions of Head & Shoulders shampoo in Surabaya.

Manajemen Pembelajaran Agama Islam dalam Meningkatkan Mutu Pendidikan pada Dayah Salafiyah Sukma Al-Mubarakah Kunyet Mule

This research aims to describe the management of jurisprudence and aqidah in improving the quality of education at Dayah Salafiyah Sukma al-Mubarakah. Starting from learning planning, organizing, coordinating, controlling and evaluating learning. This research is a qualitative research. The research subjects were school principals, teachers and students. The research location is Sukma Al-Mubarakah Kunyet Mule High School which is located on Jln. Exxon Mobil at the intersection of Cut Mutia's house. Data collection methods in this research are interviews, observation and documentation as well as data analysis using data reduction and presentation techniques. The research results show that Sukma Al-Mubarakah Kunyet Mule High School has carried out learning activities like other high schools in Dayah, but the implementation is not optimal and still requires improvement and improvement in several ways. In the student aspect, better handling is needed, and more organized facilities are arranged. Facilities and infrastructure are the most important things to achieve good quality education. That's where things become obstacles in improving the quality of education.

Intraoral scanner-based virtual facebow transferring: A chairside dental technique.

Accurately mounting dental casts on an articulator is an essential step in prosthetic treatments. In digital dentistry, virtual articulator mounting procedures rely on virtual facebow records. However, virtual facebow records usually require devices like face scanners or jaw motion tracking systems that are not commonly available in most dental practices. The present technique report describes a straightforward intraoral scanner-based virtual facebow transfer approach. In this technique, a reference facebow joint support was first scanned and aligned with a virtual articulator. Then, a patient's facebow joint support and bite fork assembly were scanned chairside with an intraoral scanner and aligned with the virtual articulator by matching common features with the reference facebow joint support. After aligning the patient's intraoral scans with the patient's bite fork scan that was already superimposed on the virtual articulator, a virtual mounting process was achieved. Once the corresponding reference facebow joint supports have been generated, this technique can be easily implemented with most facebow systems and be seamlessly integrated into daily clinical practice as only an intraoral scanner and a conventional facebow were required.

Pengelolaan Permukiman Pesisir dan di Atas Air Berkelanjutan di Desa Teluk Sunting, Kabupaten Kepulauan Anambas Provinsi Kepulauan Riau

Coastal settlements are highly vulnerable because they are greatly affected by global climate change. Population growth will further increase the use of unorganized built space in the coastal area of Teluk Sunting Village which in the future could lead to conflict as a result of regulations and policies regarding coastal areas. This research was conducted in the coastal residential area of Teluk Sunting Village, Central Siantan District, Anambas Islands Regency. The research method consists of data collection techniques, sampling techniques, data requirements, data presentation, analysis methods and data analysis techniques. The results obtained are that the administrative area of Teluk Sunting Village contains cultivation and protected areas according to the RTRW Spatial Pattern Plan. The area is 5.29 Ha of mangrove ecosystem area and 34.83 Ha of protected forest area. Area of 19.22 Ha cultivation area. The environmental quality results obtained in RT 1 settlements are low quality settlements, RT 3 is medium quality settlements and RT 2 and RT 4 settlements are high quality settlements. The conclusions obtained from the four approaches taken to identify land use patterns, namely land use patterns, land use and use with land tenure, conformity with the RTRW and conformity with the zoning of Aquatic Tourism Parks, are in accordance with the designation. The involvement of stakeholders in the management of coastal and water areas in Teluk Sunting Village is very appropriate to support and ensure the sustainability of the area in the future.

Machine Learning Based Compiler Optimization Technique

Since the last decade to upcoming era the need of machine learning based compilation approaches has become indispensable for every aspect of growing technology especially artificial intelligence and network computing areas. These approaches are used to enhance the result quality by their improved performance as well as handles compiler optimization problems namely optimization selection and phase ordering. It has enthused from a vague research place in middle of the road movement. Wealth of present compiler optimization techniques leads to finding the best heuristic parameters to tune each optimization tactic using machine learning. In this research paper, we highlighted the term machine learning and compiler along with relationship between compiler optimization and machine learning with the identity of the concept of models, training, and approaches.

A hybrid technique for impact of hybrid renewable energy systems on reliability of distribution power system

A hybrid method is proposed for the effect of hybrid renewable energy systems by using photovoltaic and wind turbines based on the reliability of power system distribution. The proposed hybrid technique is the combination of the golden jackal optimization (GJO) and white shark optimization (WSO), and hence, it is named the GJO-WSO technique. The important aim of this proposed technique is to lessen the costs of the distribution system for power outages while meeting the system's needs. The GJO-WSO technique examines how renewable-energy-sources (RESs) affect the dependability of power systems (PSs). The impact of distributed generation (DG) technologies in a power system is analyzed by utilizing reliability measures like energy not supplied and expected interruption cost, (EENS, ECOST). The market pricing, wind and solar system output power, and load uncertainty are analyzed. Next, the proposed technique's performance is evaluated with other techniques. In this, the proposed technique provides a higher accuracy of 99.68 %. So, it concluded that the proposed strategy provides optimum than the present techniques.

ALUR TAHAPAN PENGEMBANGAN KOLEKSI PADA PERPUSTAKAAN SEKOLAH TINGGI ILMU KESEHATAN HAMZAR LOMBOK TIMUR, NTB

The purpose of this study was to describe the sequential development of various phases involved in the collection development process at the Hamzar College of Health Sciences (STIKES) Library. Library collection development is an important effort to ensure relevant, contemporary, and user-oriented resources. This study uses a quantitative descriptive method. Data were collected from observations, interviews, and documentation. This study used data presentation and reduction techniques. To validate the data, extensive observations, high persistence, and triangulation were performed. The findings of this study explain the flow process of collection development at STIKES Hamzar, which consists of several different stages, including library analysis, factors that influence collection development, collection acquisition, collection selection, and collection evaluation. These stages were implemented consistently by the STIKES Hamzar Library. Notably, the types of collections that have been developed are limited to print resources. Collection development plays an important role in encouraging regular renewal and optimally contributes to the achievement of academic and research goals

Study on seismic behavior of prefabricated shear wall with pressed cone sleeves (PCSs)

Current prefabricated shear walls predominantly utilize grouting sleeves for connection, whereas an in-depth review and summary of the current state of research have indicated a 26.7 % construction defect rate in these sleeves due to present construction techniques. To solve the problem of defects, this study introduced a novel prefabricated shear wall design using vertical rebars connected via pressed cone sleeves (PCSs). Three reinforced concrete (RC) prefabricated shear walls with different vertical rebars connected and tested under cyclic horizontal load and constant vertical load to study the seismic performance, including cast-in-situ shear wall (specimen SW1), partial sleeve grouting defects shear wall (specimen SW2) and shear wall with vertical rebars connected via PCSs (PCSs-SW, specimen SW3). Key performance indicators such as failure modes, hysteresis curves, shear capacity, stiffness, ductility, energy dissipation capacity and stress characteristics were evaluated. The results of the tests revealed that SW1 and SW3 failures resulted from a combination of bending moment and shear force, whereas SW2 failure in the area of grout sleeve defects connection. Compared to the specimen SW2, the SW3 owns better shear bearing capacity, ductility, stiffness, energy dissipating capacity and reliable seismic performance. Meanwhile, SW3 and SW1 exhibited the similar seismic performance, can primarily achieve the effect of “equivalent cast-in-situ”. Finite element (FE) model of specimen SW3 were developed by using software ABAQUS, and the precision and utility of FE models verified by testing data. Using proposed FE models, an extensive parametric study was conducted to investigate the influencing factors on the seismic performance of precast fabricated RC shear wall, including axial compression ratio, concrete strength, vertical rebar strengths and vertical rebar diameters.

A three-dimensional quantitative assessment on bony growth and symmetrical recovery of mandible after decompression for unicystic ameloblastoma

Unicystic ameloblastoma (UAM) of the jaw can be effectively reduced in volume through decompression, which promotes bone regeneration and restores jaw symmetry. This study quantitatively evaluated changes in mandible volume and symmetry following decompression of mandibular UAM. This study included 17 patients who underwent surgical decompression followed by second-stage curettage for mandibular UAM. Preoperative and postoperative three-dimensional computed tomography (CT) images were collected. Bone volume and the area of cortical perforation were measured to assess bone growth during decompression. Mandibular volumetric symmetry was analyzed by calculating the volumetric ratio of the two sides of the mandible. Twelve pairs of landmarks were identified on the surface of the lesion regions, and their coordinates were used to calculate the mean asymmetry index (AI) of the mandible. Paired t-tests and the Mann–Whitney U test were used for statistical analysis, with p < 0.05 considered indicative of statistical significance. The mean duration of decompression was 9.41 ± 3.28 months. The mean bone volume increased by 8.07 ± 2.41%, and cortical perforation recovery was 71.97 ± 14.99%. The volumetric symmetry of the mandible improved significantly (p < 0.05), and a statistically significant decrease in AI was observed (p < 0.05). In conclusion, UAM decompression enhances bone growth and symmetry recovery of the mandible. The present evaluation technique is clinically useful for quantitatively assessing mandibular asymmetry.

Video-based diagnosis support system for pianists with Musician's dystonia.

Musician's dystonia is a task-specific movement disorder that deteriorates fine motor control of skilled movements in musical performance. Although this disorder threatens professional careers, its diagnosis is challenging for clinicians who have no specialized knowledge of musical performance. To support diagnostic evaluation, the present study proposes a novel approach using a machine learning-based algorithm to identify the symptomatic movements of Musician's dystonia. We propose an algorithm that identifies the dystonic movements using the anomaly detection method with an autoencoder trained with the hand kinematics of healthy pianists. A unique feature of the algorithm is that it requires only the video image of the hand, which can be derived by a commercially available camera. We also measured the hand biomechanical functions to assess the contribution of peripheral factors and improve the identification of dystonic symptoms. The proposed algorithm successfully identified Musician's dystonia with an accuracy and specificity of 90% based only on video footages of the hands. In addition, we identified the degradation of biomechanical functions involved in controlling multiple fingers, which is not specific to musical performance. By contrast, there were no dystonia-specific malfunctions of hand biomechanics, including the strength and agility of individual digits. These findings demonstrate the effectiveness of the present technique in aiding in the accurate diagnosis of Musician's dystonia.

Fault causes and its detection in standalone PV system using ANN and GEO technique

Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems.

Integrating deep learning and regression models for accurate prediction of groundwater fluoride contamination in old city in Bitlis province, Eastern Anatolia Region, Türkiye

Groundwater resources in Bitlis province and its surroundings in Türkiye’s Eastern Anatolia Region are pivotal for drinking water, yet they face a significant threat from fluoride contamination, compounded by the region’s volcanic rock structure. To address this concern, fluoride levels were meticulously measured at 30 points in June 2019 dry period and September 2019 rainy period. Despite the accuracy of present measurement techniques, their time-consuming nature renders them economically unviable. Therefore, this study aims to assess the distribution of probable geogenic contamination of groundwater and develop a robust prediction model by analyzing the relationship between predictive variables and target contaminants. In this pursuit, various machine learning techniques and regression models, including Linear Regression, Random Forest, Decision Tree, K-Neighbors, and XGBoost, as well as deep learning models such as ANN, DNN, CNN, and LSTM, were employed. Elements such as aluminum (Al), boron (B), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), phosphorus (Pb), lead (Pb), and zinc (Zn) were utilized as features to predict fluoride levels. The SelectKbest feature selection method was used to improve the accuracy of the prediction model. This method identifies important features in the dataset for different values of k and increases model efficiency. The models were able to produce more accurate predictions by selecting the most important variables. The findings highlight the superior performance of the XGBoost regressor and CNN in predicting groundwater quality, with XGBoost consistently outperforming other models, exhibiting the lowest values for evaluation metrics like mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) across different k values. For instance, when considering all features, XGBoost attained an MSE of 0.07, an MAE of 0.22, an RMSE of 0.27, a MAPE of 9.25%, and an NSE of 0.75. Conversely, the Decision Tree regressor consistently displayed inferior performance, with its maximum MSE reaching 0.11 (k = 5) and maximum RMSE of 0.33 (k = 5). Furthermore, feature selection analysis revealed the consistent significance of boron (B) and cadmium (Cd) across all datasets, underscoring their pivotal roles in groundwater contamination. Notably, in the machine learning framework evaluation, the XGBoost regressor excelled in modeling both the “all” and “rainy season” datasets, while the convolutional neural network (CNN) outperformed in the “dry season” dataset. This study emphasizes the potential of XGBoost regressor and CNN for accurate groundwater quality prediction and recommends their utilization, while acknowledging the limitations of the Decision Tree Regressor.

Pancreatoduodenectomy with colon-last approach for advanced pancreatic head cancer.

Margin-negative surgery is very important in surgical oncology. Considering margin-negative pancreatectomy is known to be essential for cure of the pancreatic cancer, pancreatoduodenectomy with combined venous vascular or arterial resection can be a potential option for margin-negative resection, especially, in era of neoadjuvant treatment with potent systemic chemotherapy. To the contrary, special attention was not paid on combined colonic resection during PD. In this article, safe surgical technique for PD with combined colonic resection is introduced, under the name of PD with "colon-last" approach. At Severance Hospital (Yonsei University College of Medicine, Seoul, Republic of Korea), between 2014 and 2021, a total of six patients underwent PD with "colon-last" approach. The surgical technique and surgical outcome are reviewed. All patients recovered without major complications (Clavien-Dindo classification grade ≥ III) after surgery, and most of them recovered after conservative treatment with postoperative pancreatic fistula biochemical leak. None of the patients were readmitted. Only the first and second cases represent cancer-related mortality, and the other patients are still alive and are being followed up. It is hoped that the present technique, PD with colon-last approach, could be helpful enhance the procedural safety in treating advanced cancer requiring PD with combined colon resection. However, its technical safety and oncologic role should be validated by many pancreatic surgeons' collaborative studies in the near future.

Integration techniques in digital video production

The aim of this paper is to present basic digital integration techniques that are applied in the television and film industry to create a final product that meets modern quality criteria. Emphasis is placed on applied methods of creating video content for various purposes, as well as on the main components of the technological equipment of a professional television studio. The scientific-applied contribution is expressed in a comparative analysis of software products for video processing (Adobe Premiere Pro and Openshot Video Editor), which is supported by step-by-step algorithms for the implementation of basic integration techniques in digital video production. The conclusions made regarding the characteristics, possibilities and advantages of the selected products are intended to support the interns in the process of their assimilation and effective use for the realization of creative projects.

Editorial

Dear Readers, It gives me great pleasure to announce the sixth regular issue of 2024. In this issue, 6 papers cover various topical aspects of computer science by 19 authors from 7 countries: Brazil, Chile, France, India, Saudi Arabia, Tunisia, and Turkey. In an ongoing effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. As always, I would like to thank all authors for their sound research and the editorial board and our guest reviewers for their extremely valuable review effort and suggestions for improvement. These contributions, together with the generous support of the consortium members, help to maintain the quality of our journal.&amp;nbsp; In this regular issue, I am very pleased to introduce the following 6 accepted articles: George Marsicano, Edna Dias Canedo, Glauco V. Pedrosa, Cristiane S. Ramos, and Rejane M. da C. Figueiredo from Brazil look in their study into digital transformation of public services in a startup-based environment by 23 focus groups and 175 participants in total. Mauricio Solar and Pablo Aguirre from Chile discuss their research on 3D chest CT processes applying a ResNet-50 model to which a new dimension of information has been added, namely a simple autoencoder. In a collaborative work between researchers from Tunisia and France, Rakia Saidi, Fethi Jarray, and Didier Schwab propose in their article a cross-encoder neural network (Cross-BERT-GRU) to deal with the semantic similarity of Arabic sentences that benefits from both the strong contextual understanding of BERT and the sequential modeling capabilities of GRU. Also in a collaborative research between institutions from Tunisia and Saudi Arabia, Nozha Jlidi, Sameh Kouni, Olfa Jemai, and Tahani Bouchrika present their research on MediaPipe with GNN for human activity recognition. G.V.Vidya Lakshmi and S. Gopikrishnan from India look into missing values research for the IoT domain and in particular present IMD-MP technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations. Last but not least, F. Didem Alay, Nagehan &amp;#304;lhan, and M. Tahir G&amp;uuml;ll&amp;uuml;o&amp;#287;lu address in their article a comparative study of data mining methods for solar radiation and temperature forecasting models. Enjoy Reading Cordially,&amp;nbsp; Christian G&amp;uuml;tl, Managing Editor Graz University of Technology, Graz, Austria

Using Optuna and Genetic Algorithm together facilitate the classification of plant leaf diseases

The identification and severity assessment of plant leaf diseases is crucial to food security and sustainable agriculture. This study shows an innovative way to improve plant leaf disease detection. The recommended method uses Optuna for parameter optimization and the Genetic Algorithm for feature selection to improve plant leaf disease identification. We do this to improve diagnosis accuracy. This method improves classification accuracy and is called ECPLDD-OGA. Modern hyper parameter optimization framework Optuna is employed. This allows classification model parameters to be fine-tuned. A systematic feature selection method is the Genetic Algorithm. It finds the most useful characteristics in the input dataset. By applying the algorithm on the data. By facilitation, the iterative process helps create a simplified and meaningful subset of features. Contrary to parameter tinkering and feature selection, empirical data suggests that utilizing Optuna and the Genetic Algorithm simultaneously improves disease identification. The updated model recognizes sick plants more accurately and generalizes better. Optimization enabled both gains. The usage of this technology can improve agricultural operations and reduce crop losses by increasing productivity. The present ECPLDD-OGA technique helps integrate hyper parameter tweaking and feature selection into machine learning-based agricultural applications.